There's a new frontier in fraud detection. Join Julie Conroy, Aite Group, and Swastik Bihani, Simility, to learn how companies are using machine-learning models and behavioral analytics across a wide variety of structured and unstructured data to accurately detect fraud and suspicious activity. You'll see how you can easily clean, transform, enrich, and deep dive into all the related suspicious activity that makes a potential transaction suspect. We'll cover how these techniques yield insights from professionals in threat detection, fraud, security, and compliance use cases.

Bayesian analysis is one of about a dozen machine learning methods typically used; other methods are logistical regression, simple linear regression, K-means clustering, decision trees and random forests. On a continuum between deep understanding of machine learning models and viewing it as a black box, Larry Lunetta, vice president of security solutions marketing for Hewlett Packard Enterprise's Aruba Networks, finds a middle ground. Here's how Larry Lunetta, vice president of security solutions marketing for Aruba Networks, describes trained and supervised machine learning: A ransomware attack, for example, typically does one of two things after it gains access to a network. While Chow can roughly explain the two machine learning algorithms they use most often in their work -- K-means clustering and simple linear regression -- he recommends leaving the front-end research to a value-added reseller (VAR).

Go programmers love Go's simplicity, ease of deployment, and tooling. But what if you want to infuse a little more intelligence in your Go applications? In this course, Pachyderm data scientist Daniel Whitenack, Ph.D., shows Go pros how to build and train a predictive machine learning app that combines native Go with a cool ML algorithm called linear regression.

Yesterday Twitter introduced a new Finance Homepage for Twitter Enterprise Data. You can learn how to make better investment decisions using Twitter data here. Founded in 2008, iSentium uses patented Natural Language Processing (NLP) to extract sentiment from unstructured social content then instantly transforms it into highly actionable indicators in Finance, Brand Management and Politics. Founded in 2008, iSentium extracts sentiment from unstructured social content and transforms it into highly actionable indicators in Finance, Brand Management and Politics.

A recent report asserts that the artificial intelligence (AI) industry will reach a compound annual growth rate of 17.2 percent by 2023. As for geography, North America is expected hold the majority of the AI industry's market share by 2023, but Europe and the Asia-Pacific region will see significant growth thanks to the rapid pace of urbanization in some areas, increasing use of smartphones, and robust automotive sectors. The big question is whether AI and automation can produce enough new jobs to ensure that the people being replaced aren't left unemployed. In the U.S., some have predicted that 7 percent of jobs will be lost to automation by 2025, and a recent study found that as many as 10 million jobs in Great Britain could be swallowed up by automation over the next 10 years.

Put another way, there's a lot of discussion around the ways people might interact with intelligent machines. Bigger shifts include machine learning algorithms that improve other machine learning algorithms. But as MI stacks become more complicated – and as open source libraries grow and individual components become more interoperable and accessible through consistent APIs – algorithms will take over aspects of the process, inserting a layer of hidden intelligence beneath the ones that interact more directly with people. Today, thanks to as-a-service infrastructure, machine learning API products, open source models and libraries, and other new resources, the barrier to MI entry is lower than ever.

The new artificial intelligence and research group at Microsoft will have more than 5,000 employees. Once he recovers, Mr. Lu will continue to act as an adviser to Satya Nadella, Microsoft's chief executive, and Bill Gates, its co-founder, Mr. Nadella said in an email to company employees Thursday. The creation of a new group at Microsoft with a focus on artificial intelligence was already planned, but the departure of Mr. Lu -- a respected computer scientist who spent a decade at Yahoo -- affected the shape of the new organization. In addition to Microsoft's Office products and other applications, Mr. Lu oversaw Bing, which is part of the new artificial intelligence and research group.

Our story starts at IBM Research in the early 1970s, where the relational database was born. Two newly minted PhDs, Donald Chamberlin and Raymond Boyce, were impressed by the relational data model but saw that the query language would be a major bottleneck to adoption. They set out to design a new query language that would be (in their own words): "more accessible to users without formal training in mathematics or computer programming." Way before the Internet, before the Personal Computer, when the programming language C was first being introduced to the world, two young computer scientists realized that, "much of the success of the computer industry depends on developing a class of users other than trained computer specialists."

As part of their Advanced Analytics Database option, Oracle data mining allows its users to discover insights, make predictions and leverage their Oracle data. The Oracle Data Miner GUI enables data analysts, business analysts and data scientists to work with data inside a database using a rather elegant drag and drop solution. Konstanz Information Miner is a user friendly, intelligible and comprehensive open-source data integration, processing, analysis and exploration platform. Being a commercial software it also includes advanced tools like Scalable processing, automation, intensive algorithms, modelling, data visualization and exploration etc.